def __init__(self, patient_id, which_set, preprocessor_path, data_dir,
leave_one_out_seizure, sample_size_second, batch_size,
default_seed=0):
"""
The Epilepsiae dataset customized for leave-one-seizure-out cross validation.
Parameters
----------
patient_id : int
Patient ID.
which_set : string
Name used to specify which partition of the dataset to be loaded (e.g., 'train', 'valid', or 'test').
If not specified, all data will be loaded.
preprocessor_path : string
File path to store the scaler for pre-processing the EEG data.
data_dir : string
Directory that store the source EEG data.
leave_one_out_seizure : int
Index of the withheld seizure.
sample_size_second : int
Number of seconds used to specify sample size.
batch_size : int
Size of the batch, used to remove a few samples to make the the number samples dividable by the batch size.
default_seed : int, optional
Seed for random.
For preprocessing, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/datasets/preprocessing.py
For customizing dataset, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/icml_2013_wrepl/emotions/emotions_dataset.py
"""
# Load data
files = ['rec_26402102/26402102_0003.mat',
'rec_26402102/26402102_0007.mat',
'rec_26402102/26402102_0008.mat',
'rec_26402102/26402102_0017.mat']
scalp_channels = np.asarray([ u'FP1',
u'FP2',
u'F3',
u'F4',
u'C3',
u'C4',
u'P3',
u'P4',
u'O1',
u'O2',
u'F7',
u'F8',
u'T3',
u'T4',
u'T5',
u'T6',
u'FZ',
u'CZ',
u'PZ' ])
# Get seizure information
seizure_info = pd.read_table(os.path.join(data_dir, 'RECORDS-WITH-SEIZURES.txt'), sep='\t')
seizure_info['filename'] = seizure_info['filename'].str.replace('.data', '.mat', case=False)
self.data_dir = data_dir
self.files = files
self.seizure_info = seizure_info
self.filter_channels = scalp_channels
self.default_seed = default_seed
self.leave_one_out_seizure = leave_one_out_seizure
self.batch_size = batch_size
X, y, n_channels, sample_size = self.load_data(which_set, sample_size_second, batch_size, preprocessor_path)
self.n_channels = n_channels
self.sample_size = sample_size
view_converter = DefaultViewConverter((1, sample_size, 1))
view_converter.set_axes(axes=['b', 0, 1, 'c'])
DenseDesignMatrix.__init__(self, X=X, y=y,
view_converter=view_converter,
axes=['b', 0, 1, 'c'])
def __init__(self,
patient_id,
which_set,
preprocessor_path,
data_dir,
leave_one_out_seizure,
sample_size_second,
batch_size,
default_seed=0):
"""
The Epilepsiae dataset customized for leave-one-seizure-out cross validation.
Parameters
----------
patient_id : int
Patient ID.
which_set : string
Name used to specify which partition of the dataset to be loaded (e.g., 'train', 'valid', or 'test').
If not specified, all data will be loaded.
preprocessor_path : string
File path to store the scaler for pre-processing the EEG data.
data_dir : string
Directory that store the source EEG data.
leave_one_out_seizure : int
Index of the withheld seizure.
sample_size_second : int
Number of seconds used to specify sample size.
batch_size : int
Size of the batch, used to remove a few samples to make the the number samples dividable by the batch size.
default_seed : int, optional
Seed for random.
For preprocessing, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/datasets/preprocessing.py
For customizing dataset, see more in
https://github.com/lisa-lab/pylearn2/blob/master/pylearn2/scripts/icml_2013_wrepl/emotions/emotions_dataset.py
"""
# Load data
files = [
'rec_26402102/26402102_0003.mat', 'rec_26402102/26402102_0007.mat',
'rec_26402102/26402102_0008.mat', 'rec_26402102/26402102_0017.mat'
]
scalp_channels = np.asarray([
u'FP1', u'FP2', u'F3', u'F4', u'C3', u'C4', u'P3', u'P4', u'O1',
u'O2', u'F7', u'F8', u'T3', u'T4', u'T5', u'T6', u'FZ', u'CZ',
u'PZ'
])
# Get seizure information
seizure_info = pd.read_table(os.path.join(data_dir,
'RECORDS-WITH-SEIZURES.txt'),
sep='\t')
seizure_info['filename'] = seizure_info['filename'].str.replace(
'.data', '.mat', case=False)
self.data_dir = data_dir
self.files = files
self.seizure_info = seizure_info
self.filter_channels = scalp_channels
self.default_seed = default_seed
self.leave_one_out_seizure = leave_one_out_seizure
self.batch_size = batch_size
X, y, n_channels, sample_size = self.load_data(which_set,
sample_size_second,
batch_size,
preprocessor_path)
self.n_channels = n_channels
self.sample_size = sample_size
view_converter = DefaultViewConverter((1, sample_size, 1))
view_converter.set_axes(axes=['b', 0, 1, 'c'])
DenseDesignMatrix.__init__(self,
X=X,
y=y,
view_converter=view_converter,
axes=['b', 0, 1, 'c'])
